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Determining 3D Flow Fields via Multi-camera Light Field Imaging
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自主监督的联合流量和深度估计通过多线索不确定性建模
Rokia Abdein1, Wei Li2, Yidan Chen1
1College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China.
概括
本研究引入了一种自我监督的框架来估计运动和3D结构,通过使用任务不一致性作为不确定性估计的学习信号来提高挑战领域的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 从动态场景中估计运动和3D结构对于计算机视觉至关重要.
- 自主监督学习为手动注释提供了具有成本效益的替代方案,但与闭塞和非刚性运动作斗争.
- 现有的方法经常用单独的启发式来处理这些挑战,从而限制了它们的有效性.
研究的目的:
- 开发一个统一的框架,用于在动态场景中进行强大的运动和深度估计.
- 为了利用任务不一致性作为自我监督学习的监督信号.
- 为了改善处理堵塞,纹理模糊性和非刚性运动.
主要方法:
- 拟议的UGFD (不确定性引导的流量和深度) 框架.
- 通过 inside-task (梯度不一致) 和 inter-task (流深刚性违规) 不一致的建模来导出密度不确定性地图.
- 引入了上下文感知不确定性 (CAU) 模块和不刚性驱动 (URD) 损失,用于指导学习和集中优化.
主要成果:
- 在KITTI基准指标上取得了最先进的表现.
- 通过对Sintel和FlyingThings3D数据集进行零射击测试,证明了强大的概括能力.
- 在一个一致的不确定性框架下,成功统一处理各种错误源.
结论:
- 提出的不确定性估计范式有效地解决了自主监督运动和深度估计的局限性.
- 通过学习评估信心,UGFD框架可以在没有地面真相数据的情况下进行可靠的估计.
- 这种方法为需要准确的3D场景理解的计算机视觉任务提供了重大进步.
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01:27Steady Flow of a Fluid Stream
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